1. Field of the Invention
The present invention relates to spatially segmenting measured values of a body, such as a patient, to interpret structural or functional components of the body; and, in particular to automatically segmenting three dimensional scan data such as Computer Tomography X-Ray (CT) scan data and echogram data.
2. Description of the Related Art
Different sensing systems are widely known and used for non-invasively probing the interior structure of bodies. For example, X-rays and X-ray-based computer-aided tomography (CT), nuclear magnetic resonance (NMR) and NMR-based magnetic resonance imagery (MRI), acoustic waves and acoustics-based ultrasound imagery (USI), positron emissions and positron emission tomography (PET), and optical waves have all been used to probe the human body and bodies of other animals. Some have been used to probe non-living bodies such as machinery, buildings, and geological features. Full and partial body scans can be constructed by assembling sequences of images and other output produced by these systems. Each body scan produced by a sensing system is herein called a measurement mode of the target body. In general, a measurement mode produces a two-dimensional (2D) image, a three dimensional (3D) volume based on a set of images with the third dimension being either a spatial dimension or time, or a full four dimensional (4D) volume based on three spatial dimensions and time. In the following, two dimensional as well as higher dimensional data sets are called images and two dimensional and higher dimensional portions of images are called volumes and regions.
Various sensing systems respond to different physical phenomena, and hence provide different information about structures and functions within the target body. However, many systems give relative intensities of measured values within the target body but do not automatically identify a particular intensity boundary with the edge of a particular structural component. For example, some human tissues of different organs have similar intensities and do not provide a sharp contrast edge in the measured values. In addition, the boundary between two structures may not correspond to a particular value of measured intensity. Therefore, it is common for one or more sets of measured values to be interpreted by a human expert or expert assisted automatic system in order to segment measured intensities into structurally or functionally significant regions (hereinafter called significant regions).
In general, the segmentation process to determine the significant regions in a set of measured values is tedious and time consuming with substantial input from a human expert. While suitable in many circumstances, there are deficiencies for some circumstances.
One deficiency is that such segmentation can not handle large data sets. Real-time three-dimensional (3D) echocardiography, an emerging trend in ultrasound imaging, allows fast convenient acquisition of volumetric images of the heart with temporal resolution sufficient to follow the evolution of each beat of a heart. The structure and motion of the left ventricle (LV) is of particular interest from the standpoint of diagnosing cardiovascular diseases. Real-time 3D echocardiography allows the capture of instantaneous motion of the entire LV for a complete cardiac cycle. For quantitative evaluation of the global and local function necessary for diagnosing various cardiovascular diseases, one must retrieve and track the shape of LV throughout the cardiac cycle. Manual segmentation for large data sets, such as those produced by real-time 3D echocardiography, remains excessively cumbersome, and thus unsuitable for routine clinical use. To realize the full potential offered by the spatiotemporal (3D space+time) data sets of real-time 3D and 4D echocardiography, a robust and accurate automatic segmentation tool for tracking the dynamic shape of the wall of the LV is not just desirable, but essential. The same deficiency affects cardiac CT segmentation as well.
Another deficiency is that segmentation with manual steps can not keep pace with body operations. For example, a CT scan may be segmented to identify a tumor in the thoracic cavity; but during treatment of the tumor, the tumor moves with the breathing of the patient. The movement can be monitored with near real time CT scans or real time ultrasound scans, but the resulting measurements would not be segmented in a timely fashion using manual approaches, and the tumor may be hard to identify and treat effectively without collateral damage to healthy surrounding tissue.
Based on the foregoing, there is a clear need for techniques to perform segmentation of real time or large numbers of images that do not suffer one or more of the deficiencies of prior art approaches.